CN117622809A - Bucket wheel machine operation AI monitoring method and server - Google Patents
Bucket wheel machine operation AI monitoring method and server Download PDFInfo
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Abstract
A bucket wheel machine operation AI monitoring method and server, apply to the automatic monitoring field, the method includes obtaining the orbit image shot by the camera; performing image recognition on the track image to determine whether the track is in an abnormal state; monitoring whether an obstacle exists in a preset range in front of the bucket wheel machine through a range finder; acquiring sound data of a walking driving mechanism of the bucket wheel machine; analyzing the frequency, the volume and the sound density of the sound data to obtain a test curve; comparing the test curve with a preset reference curve to determine whether the running driving mechanism is abnormal; and under the conditions that the track is in an abnormal state and an obstacle or abnormal running of the walking driving mechanism exists in a preset range in front of the bucket wheel machine, the bucket wheel machine is controlled to stop working. The automatic monitoring device has the effects of enabling the monitoring operation of the bucket wheel machine to be automatic and improving the reliability of the operation of the bucket wheel machine.
Description
Technical Field
The application relates to automatic monitoring, in particular to an AI monitoring method and server for bucket wheel machine operation.
Background
The bucket wheel machine, i.e. bucket wheel stacker reclaimer, is a high-efficiency loading and unloading machine for continuous conveying of large-scale dry bulk storage yards, which can be used for stacking and reclaiming materials. The bucket wheel machine mainly comprises bucket wheels, a frame and a conveying mechanism, wherein the bucket wheels can pitch and horizontally swing to replace conveying arms, and the bucket wheels are arranged at the front ends of the conveying arms.
With the increasing maturity of remote control technology, the bucket wheel machine in many ports and docks, mines and other places has all realized remote control, need not the driver to operate machinery on the scene, but the bucket wheel machine has the security risk such as derailment because of the ore heap collapses or the blanking buries track on the machine, walking track fracture in the walking process, consequently still needs to monitor and patrol personnel and carry out the supervision of equipment on the scene.
Disclosure of Invention
The application provides an AI monitoring method and a server for bucket wheel machine operation, which are used for automatically monitoring the operation of a bucket wheel machine and improving the reliability of the bucket wheel machine operation.
In a first aspect, the present application provides a bucket wheel machine operation AI monitoring method, applied to a server of a monitoring system, the monitoring system further including a camera, a range finder and an earpiece disposed on the bucket wheel machine, the method including: acquiring a track image photographed by a camera; carrying out image recognition on the track image, and determining whether the track is in an abnormal state, wherein the abnormal state comprises track coverage, track splitting and track breaking; monitoring whether an obstacle exists in a preset range in front of the bucket wheel machine through a range finder; acquiring sound data of a walking driving mechanism of the bucket wheel machine, wherein the sound data is acquired by a receiver; analyzing the frequency, the volume and the sound density of the sound data to obtain a test curve, wherein the test curve is used for representing the change relation of the frequency and the volume and the frequency and the sound density; comparing the test curve with a preset reference curve to determine whether the running driving mechanism runs abnormally, wherein the reference curve is a curve which is established by sound data when the running driving mechanism runs well and is used for representing the change relation of frequency and sound volume and frequency and sound density; and under the conditions that the track is in an abnormal state and an obstacle or abnormal running of the walking driving mechanism exists in a preset range in front of the bucket wheel machine, the bucket wheel machine is controlled to stop working.
In the above embodiment, the track image is acquired by the camera, and whether the track is abnormal, such as covered, cracked or broken, is judged through image recognition. Meanwhile, the range finder monitors whether an obstacle exists in front of the bucket wheel machine, and safe running is ensured. And acquiring sound data of the bucket wheel machine walking driving mechanism through the receiver, analyzing the frequency, the sound volume and the sound density to obtain a test curve, and judging whether the walking driving mechanism operates abnormally. And if the track is abnormal, an obstacle exists or the traveling driving mechanism is abnormal, the bucket wheel machine is controlled to stop working, so that the operation safety is ensured. The method combines a plurality of monitoring means, comprehensively monitors the running state of the bucket wheel machine by combining an artificial intelligence technology with a monitoring system, discovers abnormal conditions in advance, adopts corresponding measures, ensures the safe running of the bucket wheel machine, and is beneficial to improving the working efficiency and reducing the accident risk compared with the monitoring of equipment by patrolling personnel on site.
With reference to some embodiments of the first aspect, in some embodiments, performing image recognition on the track image to determine whether the track is in an abnormal state, where the abnormal state includes track coverage, track splitting, and track breaking, specifically including: preprocessing the track image to obtain a preprocessed image, wherein the preprocessing comprises image denoising and image enhancement operations; extracting track features in the preprocessed image, wherein the track features comprise track shapes, track colors and track textures; and comparing the track characteristics with preset normal track characteristics to determine whether the track is in an abnormal state.
In the above embodiment, the track image is preprocessed, so that the visualization effect of the track is enhanced, and the accuracy of subsequent feature extraction is improved. The track shape reflects its geometry, the track color represents a change in the track material or overlay, and the track texture reflects the detailed features of the track surface. And comparing the extracted track characteristics with preset normal track characteristics to judge whether the track accords with the normal condition or not, thereby determining whether the track is in an abnormal state or not. The rail problem can be found early, corresponding maintenance measures can be taken, and the normal operation and safety of the bucket wheel machine can be ensured.
With reference to some embodiments of the first aspect, in some embodiments, analyzing the frequency, the volume, and the sound density of the sound data to obtain a test curve specifically includes: decomposing the sound data into different frequencies by spectral analysis; calculating the average value of the amplitude of each frequency to obtain the volume; drawing according to the frequency and the volume to obtain a change relation of the frequency and the volume; dividing the volume by the width of the frequency to obtain sound density; drawing according to the frequency and the sound density to obtain a change relation of the frequency and the sound density; the change relation between frequency and volume and the change relation between frequency and sound density are used as test curves.
In the above-described embodiment, the running state of the travel drive mechanism can be estimated by analyzing the frequency, the sound volume, and the sound density, and drawing the test curve. By comparing the running driving mechanism with the reference curve, whether the running driving mechanism is abnormal or not is judged, so that corresponding measures are taken in time, and the safe running of the bucket wheel machine is ensured.
In combination with some embodiments of the first aspect, in some embodiments, after comparing the test curve with a preset reference curve, determining whether the running driving mechanism is abnormal, where the reference curve is a curve established by sound data when the running driving mechanism is well-functioning and used for characterizing a frequency and a volume and a change relation of the frequency and the sound density, the method further includes: under the condition that abnormal running of the traveling drive is determined, determining a fault component of the traveling drive according to a test curve, wherein the fault component comprises a motor, a speed reducer, a coupler and wheels; a maintenance schedule for the travel drive is determined based on the faulty assembly.
In the embodiment, the method can further determine the components which may have faults after determining that the traveling driving mechanism is abnormal in operation, and make corresponding maintenance schemes. The maintenance measures are taken in time, the fault assembly is repaired, and the normal operation of the bucket wheel machine is ensured. By accurately diagnosing the faulty components and providing corresponding maintenance schemes, downtime can be minimized, improving equipment availability and operating efficiency.
With reference to some embodiments of the first aspect, in some embodiments, the method further includes: acquiring energy consumption data and work load of the bucket wheel machine; determining an energy consumption peak period and an energy consumption valley period according to the energy consumption data, wherein the energy consumption peak period is a preset period when the total energy consumption exceeds a preset first threshold value, the energy consumption peak period is a preset period when the total energy consumption is lower than a preset second threshold value, and the preset first threshold value is larger than the preset second threshold value; and adjusting the work load of the bucket wheel machine according to the energy consumption peak period and the energy consumption valley period.
In the above embodiment, by determining the energy consumption peak period and the energy consumption valley period according to the energy consumption data and adjusting the work load of the bucket turbine accordingly, the effective utilization of energy and the optimization of energy consumption are realized. The method is beneficial to reducing the total energy consumption of the bucket wheel machine, improving the energy utilization efficiency, effectively managing the energy consumption fluctuation and further reducing the operation cost and the environmental influence.
With reference to some embodiments of the first aspect, in some embodiments, adjusting the workload of the bucket turbine according to the energy consumption peak period and the energy consumption valley period specifically includes: determining energy consumption operation in the energy consumption peak period; dividing the energy consumption operation into a peak necessary operation and a peak non-necessary operation according to a preset necessary level of the operation in a peak time period; and adjusting the unnecessary operation of the peak to a period except the energy consumption peak period.
In the above-described embodiment, by adjusting the peak unnecessary operation to a period other than the energy consumption peak period, smooth distribution of energy consumption and load balancing are achieved. The method is beneficial to reducing the total energy consumption of the energy consumption peak period, reducing the work load of the bucket turbine in the peak period and improving the energy utilization efficiency. In addition, the operation plan can be optimized, and the operation efficiency and the flexibility are improved.
With reference to some embodiments of the first aspect, in some embodiments, after the step of adjusting the workload of the bucket turbine according to the energy consumption peak period and the energy consumption valley period, the method further comprises: acquiring analysis results of energy consumption data and workload; adjusting energy-saving parameters of the bucket wheel machine according to the analysis result; after the energy-saving parameter adjustment is implemented, monitoring energy consumption data and work load of the bucket wheel machine; and evaluating the energy saving effect according to the energy consumption data and the workload.
In the embodiment, the analysis results of the energy consumption data and the work load are obtained, the energy saving parameters of the bucket wheel machine are adjusted based on the analysis results, and the effectiveness and the actual effect of the energy saving effect are evaluated by implementing the energy saving parameter adjustment and monitoring the energy consumption data and the work load, so that the energy utilization and the work efficiency of the bucket wheel machine are continuously optimized, the energy consumption cost is reduced, and the sustainability and the environmental benefit of the bucket wheel machine are improved.
In a second aspect, embodiments of the present application provide a server, including: a first acquisition module for acquiring a track image photographed by a camera;
the identification module is used for carrying out image identification on the track image, and determining whether the track is in an abnormal state or not, wherein the abnormal state comprises track coverage, track splitting and track breaking;
the monitoring module is used for monitoring whether an obstacle exists in a preset range in front of the bucket wheel machine through the range finder;
the second acquisition module is used for acquiring sound data of the walking driving mechanism of the bucket wheel machine, and the sound data is acquired by the receiver;
the analysis module is used for analyzing the frequency, the volume and the sound density of the sound data to obtain a test curve, and the test curve is used for representing the change relation of the frequency and the volume and the frequency and the sound density;
the comparison module is used for comparing the test curve with a preset reference curve to determine whether the running driving mechanism runs abnormally or not, wherein the reference curve is a curve which is established by sound data and used for representing the change relation of frequency and volume and frequency and sound density when the running driving mechanism runs well;
the control module is used for controlling the bucket wheel machine to stop working under the conditions that the track is in an abnormal state and an obstacle or a traveling driving mechanism is in a preset range in front of the bucket wheel machine to run abnormally.
In a third aspect, embodiments of the present application provide a server, including: one or more processors and memory; the memory is coupled to the one or more processors, the memory for storing computer program code comprising computer instructions that the one or more processors call to cause the server to perform the method as described in the first aspect and any possible implementation of the first aspect.
In a fourth aspect, embodiments of the present application provide a computer program product comprising instructions which, when run on a server, cause the server to perform a method as described in the first aspect and any possible implementation of the first aspect.
In a fifth aspect, embodiments of the present application provide a computer-readable storage medium comprising instructions that, when executed on a server, cause the server to perform a method as described in the first aspect and any possible implementation of the first aspect.
It will be appreciated that the servers provided in the second aspect, the third aspect, the computer program product provided in the fourth aspect, and the computer storage medium provided in the fifth aspect are each configured to perform the method provided in the embodiment of the present application. Therefore, the advantages achieved by the method can be referred to as the advantages of the corresponding method, and will not be described herein.
One or more technical solutions provided in the embodiments of the present application at least have the following technical effects or advantages:
1. due to the adoption of the camera and the image recognition technology, the image of the bucket wheel machine track can be acquired and the accurate image recognition can be carried out. By analyzing the track image, whether the track is in an abnormal state, such as track coverage, track cracking or track breaking, is determined, so that the track state is monitored more intuitively and accurately, and the running safety and stability of the bucket wheel machine are greatly improved.
2. Due to the fact that a range finder is used for monitoring whether an obstacle exists in front of the bucket wheel machine. Through the obstacle in the real-time supervision bucket wheel machine place ahead presets the within range, can in time take measures and avoid the emergence of collision accident, protect bucket wheel machine and surrounding equipment's safety for bucket wheel machine's automation mechanized operation is more reliable and safe.
3. Because the sound data is analyzed, the frequency, the volume and the sound density of the sound data of the walking driving mechanism are analyzed to obtain a test curve, and whether the walking driving mechanism operates abnormally is further judged. The state of the traveling driving mechanism can be monitored in real time, the fault component can be determined, a corresponding maintenance scheme is provided, the fault diagnosis and maintenance efficiency of the bucket wheel machine are improved, and the maintenance cost and time are saved.
Drawings
FIG. 1 is a block diagram of a bucket wheel machine in an embodiment of the present application;
FIG. 2 is an exemplary diagram of a reference curve in an embodiment of the present application;
FIG. 3 is a schematic flow chart of a method for monitoring AI operation of the bucket wheel machine in an embodiment of the application;
FIG. 4 is a schematic flow chart of an AI monitoring method for bucket wheel machine operation in an embodiment of the application;
FIG. 5 is a schematic diagram of a functional module of a server according to an embodiment of the present application;
fig. 6 is a schematic structural diagram of a physical device of a server according to an embodiment of the present application.
Detailed Description
The terminology used in the following embodiments of the application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in the specification and the appended claims, the singular forms "a," "an," "the," and "the" are intended to include the plural forms as well, unless the context clearly indicates to the contrary. It should also be understood that the term "and/or" as used in this application is intended to encompass any or all possible combinations of one or more of the listed items.
The terms "first," "second," and the like, are used below for descriptive purposes only and are not to be construed as implying or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more such feature, and in the description of embodiments of the present application, unless otherwise indicated, the meaning of "a plurality" is two or more.
For easy understanding, the application scenario of the embodiments of the present application is described below. In automated monitoring, bucket wheel machines are a common apparatus for loading and handling materials, such as loading and unloading goods at ports or transporting ores in mines. However, due to the complexity of the bucket wheel machine, potential safety hazards and fault risks exist in the running process. Therefore, ensuring safe operation of the bucket wheel machine and reducing faults are important to improving working efficiency and ensuring personnel safety.
In the related art, some bucket wheel machine operation monitoring systems use sensors and monitoring devices to monitor the operational state of the bucket wheel machine, such as monitoring the track condition using cameras or monitoring the sound of mechanical components using sound sensors. However, these methods have some drawbacks. For example, simply relying on a camera to identify an image of a track may be affected by factors such as light, occlusion, or image quality, resulting in reduced accuracy. Moreover, conventional acoustic sensors may not be able to accurately analyze acoustic data, and it may be difficult to determine whether the travel drive mechanism is operating abnormally.
By adopting the bucket wheel machine operation AI monitoring method and the server in the embodiment of the application, the track image is shot by using the camera, and the image recognition is carried out so as to judge whether the track is in an abnormal state, such as track coverage, track splitting or track breaking. Meanwhile, whether the barrier exists in front of the bucket wheel machine is monitored through the range finder, so that the safety of the bucket wheel machine is further improved. In addition, through analyzing the sound data of the walking driving mechanism and comparing the sound data with a preset reference curve, whether the walking driving mechanism operates abnormally or not is judged. In abnormal conditions, the server timely controls the bucket wheel machine to stop working, so that potential danger and faults are avoided.
As shown in fig. 1, a camera, a range finder and a receiver are mounted on the bucket wheel machine, and a server in the monitoring system is in communication connection with the camera, the range finder and the receiver.
For ease of understanding, the method provided in this embodiment is described in the following in conjunction with the above scenario. Referring to fig. 3, a flow chart of an AI monitoring method for bucket wheel machine operation in an embodiment of the present application is shown.
S301, acquiring a track image shot by a camera;
the bucket wheel machine is provided with a camera, the camera shoots a track walked by the bucket wheel machine in the walking process of the bucket wheel machine, the server is in communication connection with the camera, and the server acquires a track image shot by the camera.
S302, performing image recognition on the track image, and determining whether the track is in an abnormal state;
abnormal conditions include track coverage, track splitting, and track breaking;
the method comprises the steps of preprocessing a track image by a server, wherein the preprocessing operation comprises the steps of track image denoising, track image enhancement, track image smoothing and the like, and then the track image is analyzed through a deep learning algorithm, so that whether the track is in an abnormal state or not is identified.
Alternatively, in general, image recognition is performed on a track image, and determining whether the track is in an abnormal state may be implemented by:
Preprocessing the track image to obtain a preprocessed image, wherein the preprocessing comprises image denoising and image enhancement operations; extracting track features in the preprocessed image, wherein the track features comprise track shapes, track colors and track textures; and comparing the track characteristics with preset normal track characteristics to determine whether the track is in an abnormal state.
The orbit image is first preprocessed and a suitable denoising algorithm, such as median filtering, gaussian filtering or wavelet denoising, can be applied to reduce noise in the image. And appropriate image enhancement techniques such as histogram equalization, contrast enhancement or adaptive enhancement are employed to enhance the sharpness and contrast of the image;
then extracting the track characteristics in the preprocessed image, and applying shape analysis technology such as edge detection algorithm, hough transformation or morphological operation to detect and extract the shape information of the track; extracting color information of the track through a color analysis method, such as color space conversion, threshold segmentation, color statistics, and the like; texture analysis techniques, such as gray level co-occurrence matrix, local binary pattern or micropulse texture analysis, etc., are used to capture the texture features of the tracks.
And acquiring track images of uncovered tracks, unbroken tracks and unbroken tracks as normal track images, extracting normal track features from the normal track images, comparing the track features with preset normal track features, and evaluating the similarity degree of the track to be detected and the normal track through similarity measurement. If the difference between the track characteristics to be detected and the preset normal track characteristics is large, and the conditions of track coverage, track splitting and track breaking are indicated, the track can be determined to be in an abnormal state; if the difference between the track characteristic to be detected and the preset normal track characteristic is small, the track is not covered, cracked and broken, and the track can be determined to be in a normal state.
S303, monitoring whether an obstacle exists in a preset range in front of the bucket wheel machine through a range finder;
and installing a range finder at a proper position in front of the bucket wheel machine, determining a preset range by a server according to the working requirement and the safety standard of the bucket wheel machine, and judging whether an obstacle exists in the preset range in front of the bucket wheel machine according to the preset range.
S304, acquiring sound data of a traveling driving mechanism of the bucket wheel machine;
The sound data are acquired by the earphone;
and installing a receiver on the bucket wheel machine to receive and record the sound data of the walking driving mechanism of the bucket wheel machine, and establishing communication connection between the server and the receiver to acquire the sound data of the walking driving mechanism of the bucket wheel machine recorded and collected by the receiver.
S305, analyzing the frequency, volume and sound density of the sound data to obtain a test curve;
the test curve is used for representing the change relation between the frequency and the volume and the change relation between the frequency and the sound density;
first, sound data is preprocessed using audio processing software, the preprocessing including noise removal, filtering, and signal enhancement operations to improve data quality.
The sound data is then converted from a time domain representation to a frequency domain representation using a fourier transform, thereby seeing components of different frequencies. And calculating a spectrogram of the sound data, and determining main frequency components and frequency ranges of the sound data according to the spectrogram. The frequency represents the vibration periodicity of the sound wave, i.e. the number of rapid vibration repetitions in the sound wave.
Next, the volume of the sound data is calculated using audio processing software or a volume measuring tool. Volume refers to the intensity or loudness of sound, and indicates the relative magnitude or intensity of sound. Which is typically related to the amplitude of the acoustic waveform. A larger amplitude corresponds to a higher volume, while a smaller amplitude corresponds to a lower volume.
And, using the time window and overlapping method, the sound data is divided into shorter time periods, and the sound energy in each time period is calculated. The sound density is calculated from the sound energy during the time period. The sound density refers to the energy or intensity of sound in a unit frequency range, and reflects the frequency distribution of sound, i.e. the distribution density of sound at different frequencies.
Finally, the frequency, volume and sound density are integrated into a test curve. These curves can be plotted on the same graph with frequency on the horizontal axis and volume and sound density on the vertical axis.
Alternatively, in general, the frequency, volume and sound density of the sound data are analyzed, and the test curve may be obtained by the following manner:
decomposing the sound data into different frequencies by spectral analysis; calculating the average value of the amplitude of each frequency to obtain the volume; drawing according to the frequency and the volume to obtain a change relation of the frequency and the volume; dividing the volume by the width of the frequency to obtain sound density; drawing according to the frequency and the sound density to obtain a change relation of the frequency and the sound density; the change relation between frequency and volume and the change relation between frequency and sound density are used as test curves.
S306, comparing the test curve with a preset reference curve to determine whether the running driving mechanism is abnormal;
the reference curve is a curve for representing the relationship between frequency and volume and the relationship between frequency and sound density, which is established by sound data when the action driving mechanism is well operated, as shown in fig. 2, and fig. 2 is an exemplary diagram of a preset reference curve.
The difference between the test curve and the reference curve is compared by whether the frequency and the volume and the trend of the frequency and the sound density are similar.
And judging the running state of the walking driving mechanism according to the difference between the test curve and the reference curve. If the test curve is similar to the reference curve and there is no significant deviation or anomaly, the travel drive may be operating properly. In contrast, if there is a significant difference or anomaly in the test curve from the reference curve, there may be an operational anomaly in the travel drive mechanism.
S307, when the track is in an abnormal state and an obstacle or abnormal running of the traveling driving mechanism exists in a preset range in front of the bucket wheel machine, the bucket wheel machine is controlled to stop working.
Under the conditions that the track is in an abnormal state and an obstacle or abnormal running of the running driving mechanism exists in a preset range in front of the bucket wheel machine, the server sends a stop instruction to the running driving mechanism of the bucket wheel machine, so that the bucket wheel machine stops working.
The following supplements the scenario of the present embodiment.
And the server adjusts the working load of the bucket wheel machine according to the energy consumption peak period and the energy consumption valley period by combining the energy consumption data and the working load so as to achieve the aim of saving energy. And the energy-saving parameters of the bucket wheel machine are further optimized by analyzing the energy consumption data and the evaluation result of the work load, so that the energy utilization efficiency of the bucket wheel machine is improved.
In combination with the above scenario, a further more specific flow of the method provided in this embodiment will be described below. Referring to fig. 4, another flow chart of the AI monitoring method for bucket wheel machine operation in the embodiment of the present application is shown.
The following steps may be performed between step S306 and step S307 in the embodiment shown in fig. 4:
s401, under the condition that running abnormality of the walking drive is determined, determining a fault component of the walking drive according to a test curve;
the fault assembly comprises a motor, a speed reducer, a coupler and wheels;
the test curve under abnormal conditions is compared with the reference curve under normal conditions. The test curve under abnormal conditions may exhibit significantly different patterns and characteristics than the reference curve under normal conditions to determine components that may be faulty. For example, if the test curve under an abnormal condition shows an abnormal amplitude or abnormal increase in sound density over a particular frequency range, then components associated with that frequency range may fail.
S402, determining a maintenance scheme of the traveling drive according to the fault assembly;
corresponding travel drive maintenance schemes are formulated according to the determined fault components, and the fault components and possible maintenance schemes thereof are listed below:
if the motor fails, the corresponding maintenance scheme is as follows:
1) And (5) checking the power supply and connection conditions of the motor, and ensuring that the power supply is stable and well connected.
2) The winding and insulation condition of the motor is checked, and damaged windings or insulation materials are repaired or replaced.
3) And checking the bearing and lubrication condition of the motor, and cleaning, lubricating or replacing the bearing.
If the speed reducer fails, the corresponding maintenance scheme is as follows:
1) The gears and bearings of the reducer are inspected, and the damaged gears or bearings are repaired or replaced.
2) And (5) checking the lubrication condition of the speed reducer, and replacing lubricating oil and cleaning a lubricating system.
If the coupler fails, the corresponding maintenance scheme is as follows:
1) And (5) checking the connection state of the coupler, and ensuring firm connection between the coupler and the adjacent component.
2) And checking the shaft hole and the key groove of the coupler, and repairing or replacing the damaged part.
3) And adjusting the centering condition of the coupler to ensure that the centering of the axis of the coupler meets the requirement.
If the wheel fails, the corresponding maintenance scheme is as follows:
1) The tread wear condition of the wheel is checked, and whether the wheel needs to be replaced is determined according to the wear degree.
2) The bearings and axle bores of the wheel are inspected, and the damaged portion is repaired or replaced.
3) Dynamic balance of the wheels is carried out to ensure stable running.
It will be appreciated that in some embodiments, steps S401-S402 may not be performed, so that step S307 may be performed directly after step S306 is performed, which is not limited herein.
After step S307 of the embodiment shown in fig. 4, the following steps may be performed:
s403, acquiring energy consumption data and work load of the bucket wheel machine;
the monitoring system in the bucket wheel machine can monitor the state and performance parameters of the bucket wheel machine in real time, including an energy consumption monitoring function and a work load monitoring function, so that the server acquires the energy consumption data and the work load of the bucket wheel machine.
S404, determining an energy consumption peak period and an energy consumption valley period according to the energy consumption data;
the energy consumption peak time period is a preset time period when the total energy consumption exceeds a preset first threshold value, the energy consumption peak time period is a preset time period when the total energy consumption is lower than a preset second threshold value, and the preset first threshold value is larger than the preset second threshold value;
the preset first threshold and the preset second threshold are set according to the actual situation and the requirements, for example, based on the percentage of the energy consumption level or the specific energy consumption value.
And analyzing the collected energy consumption data, calculating to obtain the total energy consumption in each time period, and comparing the total energy consumption with a preset threshold value.
Identifying time periods when the total energy consumption exceeds a preset first threshold value, wherein the time periods are energy consumption peak time periods; and identifying time periods in which the total energy consumption is lower than a preset second threshold value, wherein the time periods are energy consumption valley periods.
S405, adjusting the work load of the bucket wheel machine according to the energy consumption peak period and the energy consumption valley period;
and during the energy consumption peak period, the allocation or the priority of the work tasks is adjusted, and during the energy consumption peak period, the work tasks with high priority are carried out so as to balance the work load of each bucket wheel machine. And under the condition of ensuring the implementation, the work task in the energy consumption peak period is adjusted to the energy consumption valley period to be carried out so as to avoid the energy consumption peak value in the energy consumption peak period.
In the energy consumption low valley period, the server controls the bucket wheel machine to carry out a work task with a larger load so as to fully utilize the advantage of the energy consumption low valley period. Meanwhile, the server can also determine the low energy consumption period as a maintenance period for the bucket wheel machine, so that people can lubricate, clean and check the state of the bucket wheel machine in the maintenance period to ensure the good running state of the bucket wheel machine.
Alternatively, in general, adjusting the workload of the bucket wheel machine according to the energy consumption peak period and the energy consumption valley period may be achieved by:
determining energy consumption operation in the energy consumption peak period; dividing the energy consumption operation into a peak necessary operation and a peak non-necessary operation according to a preset necessary level of the operation in a peak time period; and adjusting the unnecessary operation of the peak to a period except the energy consumption peak period.
And analyzing the acquired energy consumption data and the workload to determine the time period of the energy consumption peak time. And identifying the energy consumption operation related to the energy consumption peak period according to the association of the workload and the energy consumption data. These energy consuming operations may be work tasks performed during peak energy consumption periods, resulting in higher energy consumption of the bucket wheel.
The energy consumption operation is divided into a peak necessary operation and a peak unnecessary operation according to the importance and the emergency degree of the energy consumption operation. The peak-necessary operation is a critical task that must be performed during the peak period of energy consumption, and the peak-unnecessary operation may be performed during a period other than the peak period of energy consumption.
The tasks divided into the peak unnecessary jobs are adjusted to the periods other than the energy consumption peak periods. By rescheduling the work schedule and workflow, these peak unnecessary operations are preferentially scheduled to be performed during the low energy consumption period or the off-peak period, so as to reduce the energy consumption of the bucket turbine during the high energy consumption period.
S406, acquiring analysis results of energy consumption data and workload;
and cleaning and arranging the acquired energy consumption data and workload, including removing abnormal values, filling missing data, standardizing data formats and the like.
And analyzing the total energy consumption, the energy consumption trend, the energy consumption peak value and the like in the energy consumption data by adopting a statistical analysis method, a time sequence analysis or other related methods.
And analyzing the contents of the load change trend, the load distribution, the load peak value and the like in the workload by adopting a statistical analysis method, a pattern recognition technology or other related methods.
The analysis results of the energy consumption data and the workload are visually displayed, and the analysis results can be presented in the forms of charts, graphs, reports and the like.
S407, adjusting energy-saving parameters of the bucket wheel machine according to the analysis result;
and according to the analysis result of the working load, evaluating whether the current working mode of the bucket wheel machine is suitable for actual requirements. If the bucket wheel machine has the conditions of excessive work or low work efficiency, the server adjusts the energy-saving parameters of the bucket wheel machine according to the preset energy-saving parameter adjusting table, so that the bucket wheel machine is more efficient. For example, the speed, acceleration, dwell time, etc. parameters of the bucket wheel machine are adjusted to accommodate the actual workload demands.
The energy-saving parameter adjustment table comprises various parameters which influence the energy consumption of the bucket wheel and provides corresponding adjustment ranges or values. These parameters include speed, acceleration, dwell time, mode of operation, load sharing, etc. of the bucket wheel machine.
And according to the analysis result of the energy consumption data, the energy efficiency condition of equipment used by the bucket wheel machine is estimated. If the bucket wheel machine is low in energy, the server plans to update or upgrade the bucket wheel machine. For example, to more energy efficient motors, to retrofit hydraulic systems, etc.
S408, after energy-saving parameter adjustment is implemented, monitoring energy consumption data and work load of the bucket wheel machine;
after the energy-saving parameter adjustment is implemented, the state and the performance parameters of the bucket wheel machine are monitored through a monitoring system in the bucket wheel machine, wherein the monitoring system comprises an energy consumption monitoring function and a work load monitoring function, so that the server acquires the energy consumption data and the work load of the bucket wheel machine.
S409, evaluating the energy saving effect according to the energy consumption data and the workload.
And analyzing and comparing the energy consumption data and the workload data before and after the energy saving parameter adjustment is implemented. By comparing the total energy consumption levels before and after adjustment, whether the energy consumption after adjustment is obviously reduced or not is ensured under the same working load condition; analyzing trend changes of energy consumption data, such as daily, weekly or monthly energy consumption changes, and observing whether obvious energy conservation trend exists; analyzing the peak condition in the energy consumption data, and judging whether the peak energy consumption is reduced or not; the workload change conditions before and after adjustment, such as load distribution, load peak, etc., are compared. And whether the adjusted workload is more reasonable and balanced is observed.
And calculating the related index of the energy saving effect through analysis and comparison. For example, indexes such as a reduction ratio of energy consumption, energy consumption per unit output and the like are calculated to quantify the energy saving effect.
It will be appreciated that in some embodiments, steps S403-S409 may not be performed, and thus may end directly after step S307 is performed, which is not limited herein.
The server in the embodiment of the present application is described below from the viewpoint of a module. Fig. 5 is a schematic structural diagram of a functional module of a server according to an embodiment of the present application.
The server includes:
a first acquisition module 501 for acquiring a track image photographed by a camera;
the identifying module 502 is configured to perform image identification on the track image, and determine whether the track is in an abnormal state, where the abnormal state includes track coverage, track splitting, and track breaking;
the monitoring module 503 is configured to monitor whether an obstacle exists in a preset range in front of the bucket wheel machine through the range finder;
the second obtaining module 504 is configured to obtain sound data of the walking driving mechanism of the bucket wheel machine, where the sound data is collected by the receiver;
the analysis module 505 is configured to analyze the frequency, the volume and the sound density of the sound data to obtain a test curve, where the test curve is used to characterize the change relationship between the frequency and the volume and between the frequency and the sound density;
The comparison module 506 is configured to compare the test curve with a preset reference curve, and determine whether the running driving mechanism is abnormal, where the reference curve is a curve that is established by sound data when the running driving mechanism is well running and is used for representing a change relationship between frequency and volume and between frequency and sound density;
the control module 507 is used for controlling the bucket wheel machine to stop working under the condition that the track is in an abnormal state and an obstacle or a running driving mechanism is in a preset range in front of the bucket wheel machine.
In some embodiments, the identification module 502 is specifically configured to:
preprocessing the track image to obtain a preprocessed image, wherein the preprocessing comprises image denoising and image enhancement operations;
extracting track features in the preprocessed image, wherein the track features comprise track shapes, track colors and track textures;
and comparing the track characteristics with preset normal track characteristics to determine whether the track is in an abnormal state.
In some embodiments, the analysis module 505 is specifically configured to:
decomposing the sound data into different frequencies by spectral analysis;
calculating the average value of the amplitude of each frequency to obtain the volume;
drawing according to the frequency and the volume to obtain a change relation of the frequency and the volume;
Dividing the volume by the width of the frequency to obtain sound density;
drawing according to the frequency and the sound density to obtain a change relation of the frequency and the sound density;
the change relation between frequency and volume and the change relation between frequency and sound density are used as test curves.
In some embodiments, the server further comprises a determining module, specifically configured to:
under the condition that abnormal running of the traveling drive is determined, determining a fault component of the traveling drive according to a test curve, wherein the fault component comprises a motor, a speed reducer, a coupler and wheels;
a maintenance schedule for the travel drive is determined based on the faulty assembly.
In some embodiments, the server further comprises an adjustment module, specifically configured to:
acquiring energy consumption data and work load of the bucket wheel machine;
determining an energy consumption peak period and an energy consumption valley period according to the energy consumption data, wherein the energy consumption peak period is a preset period when the total energy consumption exceeds a preset first threshold value, the energy consumption peak period is a preset period when the total energy consumption is lower than a preset second threshold value, and the preset first threshold value is larger than the preset second threshold value;
and adjusting the work load of the bucket wheel machine according to the energy consumption peak period and the energy consumption valley period.
In some embodiments, the adjustment module is further specifically configured to:
Determining energy consumption operation in the energy consumption peak period;
dividing the energy consumption operation into a peak necessary operation and a peak non-necessary operation according to a preset necessary level of the operation in a peak time period;
and adjusting the unnecessary operation of the peak to a period except the energy consumption peak period.
In some embodiments, the server further comprises an evaluation module, specifically for:
acquiring analysis results of energy consumption data and workload;
adjusting energy-saving parameters of the bucket wheel machine according to the analysis result;
after the energy-saving parameter adjustment is implemented, monitoring energy consumption data and work load of the bucket wheel machine;
and evaluating the energy saving effect according to the energy consumption data and the workload.
The server in the embodiment of the present application is described above from the point of view of the modularized functional entity, and the server in the embodiment of the present application is described below from the point of view of hardware processing, please refer to fig. 6, which is a schematic structural diagram of an entity device of the server in the embodiment of the present application.
It should be noted that the structure of the server shown in fig. 6 is only an example, and should not limit the functions and the application scope of the embodiments of the present invention.
As shown in fig. 6, the server includes a central processing unit (Central Processing Unit, CPU) 601, which can perform various appropriate actions and processes according to a program stored in a Read-Only Memory (ROM) 602 or a program loaded from a storage section 608 into a random access Memory (Random Access Memory, RAM) 603, for example, performing the method described in the above embodiment. In the RAM 603, various programs and data required for system operation are also stored. The CPU 601, ROM 602, and RAM 603 are connected to each other through a bus 604. An Input/Output (I/O) interface 605 is also connected to bus 604.
The following components are connected to the I/O interface 605: an input section 606 including a camera or the like; an output portion 607 including a liquid crystal display (Liquid Crystal Display, LCD), a microphone, and the like; a storage section 608 including a hard disk and the like; and a communication section 609 including a network interface card such as a LAN (Local Area Network ) card, a modem, or the like. The communication section 609 performs communication processing via a network such as the internet. The drive 610 is also connected to the I/O interface 605 as needed. Removable media 611 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is installed as needed on drive 610 so that a computer program read therefrom is installed as needed into storage section 608.
In particular, according to embodiments of the present invention, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising a computer program for performing the method shown in the flowchart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication portion 609, and/or installed from the removable medium 611. When executed by a Central Processing Unit (CPU) 601, the computer program performs the various functions defined in the present invention.
It should be noted that, the computer readable medium shown in the embodiments of the present invention may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-Only Memory (ROM), an erasable programmable read-Only Memory (Erasable Programmable Read Only Memory, EPROM), flash Memory, an optical fiber, a portable compact disc read-Only Memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present invention, however, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with a computer-readable computer program embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing.
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. Where each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
Specifically, the server of the embodiment includes a processor and a memory, and the memory stores a computer program, and when the computer program is executed by the processor, the bucket wheel machine operation AI monitoring method provided in the embodiment is implemented.
As another aspect, the present invention also provides a computer-readable storage medium, which may be contained in the server described in the above embodiment; or may exist alone without being assembled into the server. The storage medium carries one or more computer programs which, when executed by a processor of the server, cause the server to implement the methods provided in the embodiments described above.
The above embodiments are merely for illustrating the technical solution of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present application.
As used in the above embodiments, the term "when …" may be interpreted to mean "if …" or "after …" or "in response to determination …" or "in response to detection …" depending on the context. Similarly, the phrase "at the time of determination …" or "if detected (a stated condition or event)" may be interpreted to mean "if determined …" or "in response to determination …" or "at the time of detection (a stated condition or event)" or "in response to detection (a stated condition or event)" depending on the context.
In the above embodiments, it may be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, produces a flow or function in accordance with embodiments of the present application, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, from a website, computer, server, or data center to another website, computer, server, or data center by a wired (e.g., coaxial cable, fiber optic, digital subscriber line), or wireless (e.g., infrared, wireless, microwave, etc.). The computer readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that contains an integration of one or more available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid state disk), etc.
Those of ordinary skill in the art will appreciate that implementing all or part of the above-described method embodiments may be accomplished by a computer program to instruct related hardware, the program may be stored in a computer readable storage medium, and the program may include the above-described method embodiments when executed. And the aforementioned storage medium includes: ROM or random access memory RAM, magnetic or optical disk, etc.
Claims (10)
1. A bucket wheel machine operation AI monitoring method, characterized by being applied to a server of a monitoring system, wherein the monitoring system further comprises a camera, a range finder and a receiver arranged on a bucket wheel machine, and the method comprises the following steps:
acquiring a track image photographed by the camera;
performing image recognition on the track image, and determining whether the track is in an abnormal state, wherein the abnormal state comprises the track covered, the track split and the track split;
monitoring whether an obstacle exists in a preset range in front of the bucket wheel machine or not through the range finder;
acquiring sound data of a walking driving mechanism of the bucket wheel machine, wherein the sound data is acquired by the receiver;
Analyzing the frequency, the volume and the sound density of the sound data to obtain a test curve, wherein the test curve is used for representing the change relation of the frequency and the volume and the frequency and the sound density;
comparing the test curve with a preset reference curve to determine whether the running driving mechanism runs abnormally, wherein the reference curve is a curve which is established by sound data when the action driving mechanism runs well and is used for representing the change relation of frequency and volume and frequency and sound density;
and controlling the bucket wheel machine to stop working under the condition that the track is in an abnormal state, an obstacle exists in a preset range in front of the bucket wheel machine or the running driving mechanism runs abnormally.
2. The method according to claim 1, wherein the image recognition of the track image determines whether the track is in an abnormal state, the abnormal state including the track being covered, the track being cracked, and the track being broken, specifically comprising:
preprocessing the track image to obtain a preprocessed image, wherein the preprocessing comprises image denoising and image enhancement operations;
extracting track features in the preprocessed image, wherein the track features comprise track shapes, track colors and track textures;
And comparing the track characteristics with preset normal track characteristics to determine whether the track is in an abnormal state.
3. The method according to claim 1, wherein the analyzing the frequency, volume and sound density of the sound data to obtain a test curve specifically comprises:
decomposing the sound data into different frequencies by spectral analysis;
calculating the average value of the amplitude of each frequency to obtain the volume;
drawing according to the frequency and the volume to obtain a change relation of the frequency and the volume;
dividing the volume by the width of the frequency to obtain sound density;
drawing according to the frequency and the sound density to obtain a change relation of the frequency and the sound density;
and taking the change relation of the frequency and the volume and the change relation of the frequency and the sound density as a test curve.
4. The method according to claim 1, wherein after the step of comparing the test curve with a preset reference curve, which is a curve for characterizing a frequency and a volume and a change relation of a frequency and a sound density established by sound data when the action driving mechanism is operating well, the method further comprises:
Under the condition that the running of the running drive is abnormal, determining a fault component of the running drive according to the test curve, wherein the fault component comprises a motor, a speed reducer, a coupler and wheels;
and determining a maintenance scheme of the walking drive according to the fault assembly.
5. The method according to claim 1, wherein the method further comprises:
acquiring energy consumption data and work load of the bucket wheel machine;
determining an energy consumption peak period and an energy consumption valley period according to the energy consumption data, wherein the energy consumption peak period is a preset period when the total energy consumption exceeds a preset first threshold value, the energy consumption peak period is a preset period when the total energy consumption is lower than a preset second threshold value, and the preset first threshold value is larger than the preset second threshold value;
and adjusting the working load of the bucket wheel machine according to the energy consumption peak period and the energy consumption valley period.
6. The method according to claim 5, wherein said adjusting the working load of the bucket wheel machine according to the energy consumption peak period and the energy consumption valley period, comprises:
determining energy consumption operation in the energy consumption peak time;
dividing the energy consumption operation into a peak necessary operation and a peak unnecessary operation according to a preset necessary level of the operation in a peak time period;
And adjusting the unnecessary operation of the peak to a period except the energy consumption peak period.
7. The method of claim 5, wherein after the step of adjusting the duty of the bucket turbine based on the energy consumption peak period and the energy consumption valley period, the method further comprises:
acquiring analysis results of the energy consumption data and the workload;
adjusting the energy-saving parameters of the bucket wheel machine according to the analysis result;
after the energy-saving parameter adjustment is implemented, monitoring energy consumption data and workload of the bucket wheel machine;
and evaluating the energy saving effect according to the energy consumption data and the workload.
8. A server, comprising:
a first acquisition module for acquiring a track image photographed by a camera;
the identification module is used for carrying out image identification on the track image and determining whether the track is in an abnormal state, wherein the abnormal state comprises the track covered, the track split and the track split;
the monitoring module is used for monitoring whether an obstacle exists in a preset range in front of the bucket wheel machine through the range finder;
the second acquisition module is used for acquiring sound data of the walking driving mechanism of the bucket wheel machine, and the sound data is acquired by the receiver;
The analysis module is used for analyzing the frequency, the volume and the sound density of the sound data to obtain a test curve, and the test curve is used for representing the change relation of the frequency and the volume and the frequency and the sound density;
the comparison module is used for comparing the test curve with a preset reference curve to determine whether the running driving mechanism runs abnormally or not, wherein the reference curve is a curve which is established by sound data and used for representing the change relation of frequency and volume and frequency and sound density when the running driving mechanism runs well;
the control module is used for controlling the bucket wheel machine to stop working under the conditions that the track is in an abnormal state, an obstacle exists in a preset range in front of the bucket wheel machine or the running driving mechanism runs abnormally.
9. A server, comprising: one or more processors and memory;
the memory is coupled to the one or more processors, the memory for storing computer program code comprising computer instructions that the one or more processors invoke to cause the server to perform the method of any of claims 1-7.
10. A computer readable storage medium comprising instructions which, when run on a server, cause the server to perform the method of any of claims 1-7.
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